{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "88fe760f",
   "metadata": {},
   "source": [
    "## Activation Addition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcef5dfe",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/frankaging/pyvene/blob/main/tutorials/basic_tutorials/Add_Activations_to_Streams.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "682d1fdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "__author__ = \"Zhengxuan Wu\"\n",
    "__version__ = \"10/06/2023\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3deec495",
   "metadata": {},
   "source": [
    "### Overview\n",
    "\n",
    "Interventions have many types: (1) activation swapping, (2) activation addition, or (3) any other kind of operations that modify the activation. In this tutorial, we show how we ca do activation addition."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17476fc2",
   "metadata": {},
   "source": [
    "### Set-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c34ae314",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-01-11 00:31:07,569] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    # This library is our indicator that the required installs\n",
    "    # need to be done.\n",
    "    import pyvene\n",
    "\n",
    "except ModuleNotFoundError:\n",
    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e5cb9eb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import pandas as pd\n",
    "from pyvene.models.basic_utils import embed_to_distrib, top_vals, format_token\n",
    "from pyvene import (\n",
    "    IntervenableModel,\n",
    "    AdditionIntervention,\n",
    "    SubtractionIntervention,\n",
    "    RepresentationConfig,\n",
    "    IntervenableConfig,\n",
    ")\n",
    "from pyvene.models.gpt2.modelings_intervenable_gpt2 import create_gpt2\n",
    "\n",
    "%config InlineBackend.figure_formats = ['svg']\n",
    "from plotnine import (\n",
    "    ggplot,\n",
    "    geom_tile,\n",
    "    aes,\n",
    "    facet_wrap,\n",
    "    theme,\n",
    "    element_text,\n",
    "    geom_bar,\n",
    "    geom_hline,\n",
    "    scale_y_log10,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d4487e4",
   "metadata": {},
   "source": [
    "### Factual recall with our intervenable module directly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1fc15f36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded model\n"
     ]
    }
   ],
   "source": [
    "def activation_addition_position_config(model_type, intervention_type, n_layer):\n",
    "    config = IntervenableConfig(\n",
    "        model_type=model_type,\n",
    "        representations=[\n",
    "            RepresentationConfig(\n",
    "                i,                 # layer\n",
    "                intervention_type, # component\n",
    "                \"pos\",             # intervention unit\n",
    "                1,                 # max number of unit\n",
    "            )\n",
    "            for i in range(n_layer)\n",
    "        ],\n",
    "        intervention_types=AdditionIntervention,\n",
    "    )\n",
    "    return config\n",
    "\n",
    "\n",
    "config, tokenizer, gpt = create_gpt2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "151ded21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The capital of Spain is\n",
      "_Madrid              0.10501234978437424\n",
      "_the                 0.0949699655175209\n",
      "_Barcelona           0.0702790841460228\n",
      "_a                   0.04010068252682686\n",
      "_now                 0.02824278175830841\n",
      "_in                  0.02759990654885769\n",
      "_Spain               0.022991720587015152\n",
      "_Catalonia           0.018823225051164627\n",
      "_also                0.018689140677452087\n",
      "_not                 0.01735665090382099\n",
      "\n",
      "The capital of Italy is\n",
      "_Rome                0.15734916925430298\n",
      "_the                 0.07316355407238007\n",
      "_Milan               0.046878915280103683\n",
      "_a                   0.03449810668826103\n",
      "_now                 0.03200329467654228\n",
      "_in                  0.02306535840034485\n",
      "_also                0.02274816483259201\n",
      "_home                0.01920313946902752\n",
      "_not                 0.01640527881681919\n",
      "_Italy               0.01577090471982956\n"
     ]
    }
   ],
   "source": [
    "config = activation_addition_position_config(\n",
    "    type(gpt), \"mlp_output\", gpt.config.n_layer\n",
    ")\n",
    "\n",
    "intervenable = IntervenableModel(config, gpt)\n",
    "\n",
    "base = \"The capital of Spain is\"\n",
    "source = \"The capital of Italy is\"\n",
    "inputs = [tokenizer(base, return_tensors=\"pt\"), tokenizer(source, return_tensors=\"pt\")]\n",
    "print(base)\n",
    "res = intervenable(inputs[0])[0]\n",
    "distrib = embed_to_distrib(gpt, res.last_hidden_state, logits=False)\n",
    "top_vals(tokenizer, distrib[0][-1], n=10)\n",
    "print()\n",
    "print(source)\n",
    "res = intervenable(inputs[1])[0]\n",
    "distrib = embed_to_distrib(gpt, res.last_hidden_state, logits=False)\n",
    "top_vals(tokenizer, distrib[0][-1], n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79cb7ebc",
   "metadata": {},
   "source": [
    "### We add a word embedding to all MLP streams at the last position\n",
    "In other tutorials, we often pass in `sources` where each of the example is drawn from the training data. Another way to do patching is, instead of passing in real input example, we pass in activations. These activations can be designed off-line in some particular ways."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0481a874",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can patch mlp with the rome word embedding\n",
    "rome_token_id = tokenizer(\" Rome\")[\"input_ids\"][0]\n",
    "rome_embedding = (\n",
    "    gpt.wte(torch.tensor(rome_token_id)).clone().unsqueeze(0).unsqueeze(0)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "78d9a0be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_Rome                0.4558262228965759\n",
      "_Madrid              0.2788238823413849\n",
      "_Barcelona           0.10828061401844025\n",
      "_Valencia            0.015630871057510376\n",
      "_Lisbon              0.008415448479354382\n",
      "_the                 0.006678737234324217\n",
      "_Santiago            0.006526812445372343\n",
      "_Naples              0.0041163465939462185\n",
      "_Florence            0.003120437264442444\n",
      "_Athens              0.0028584974352270365\n"
     ]
    }
   ],
   "source": [
    "base = \"The capital of Spain is\"\n",
    "\n",
    "_, counterfactual_outputs = intervenable(\n",
    "    base=tokenizer(base, return_tensors=\"pt\"),\n",
    "    unit_locations={\n",
    "        \"sources->base\": 4\n",
    "    },  # last position\n",
    "    source_representations=rome_embedding,\n",
    ")\n",
    "distrib = embed_to_distrib(gpt, counterfactual_outputs.last_hidden_state, logits=False)\n",
    "top_vals(tokenizer, distrib[0][-1], n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e6e5ff6",
   "metadata": {},
   "source": [
    "If you are interested by this work, you can simply think token embeddings at each layer are moved toward the token `_Rome` via the activation addition. Obviouosly, the LM head (which is tied with the embedding matrix) is going to pick out the most similar vectors, which are `_Rome` at the end, and some other countries since they are close to `_Rome`.\n",
    "\n",
    "You can also read more about this in this paper: [Language Models Implement Simple Word2Vec-style Vector Arithmetic](https://arxiv.org/abs/2305.16130)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0784203e",
   "metadata": {},
   "source": [
    "### Let's have a more systematic analysis of the addition effect of MLP and MHA streams\n",
    "We add the word embedding till the `i`-th layer of these streams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fe8195d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# should finish within 1 min with a standard 12G GPU\n",
    "tokens = tokenizer.encode(\" Madrid Rome\")\n",
    "base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
    "\n",
    "data = []\n",
    "for till_layer_i in range(gpt.config.n_layer):\n",
    "    config = activation_addition_position_config(\n",
    "        type(gpt), \"mlp_output\", till_layer_i + 1\n",
    "    )\n",
    "    intervenable = IntervenableModel(config, gpt)\n",
    "    for pos_i in range(len(base.input_ids[0])):\n",
    "        _, counterfactual_outputs = intervenable(\n",
    "            base,\n",
    "            unit_locations={\"sources->base\": pos_i},\n",
    "            source_representations=rome_embedding,\n",
    "        )\n",
    "        distrib = embed_to_distrib(\n",
    "            gpt, counterfactual_outputs.last_hidden_state, logits=False\n",
    "        )\n",
    "        for token in tokens:\n",
    "            data.append(\n",
    "                {\n",
    "                    \"token\": format_token(tokenizer, token),\n",
    "                    \"prob\": float(distrib[0][-1][token]),\n",
    "                    \"layer\": f\"mlp_o{till_layer_i}\",\n",
    "                    \"pos\": pos_i,\n",
    "                    \"type\": \"mlp_output\",\n",
    "                }\n",
    "            )\n",
    "\n",
    "    config = activation_addition_position_config(\n",
    "        type(gpt), \"attention_output\", till_layer_i + 1\n",
    "    )\n",
    "    intervenable = IntervenableModel(config, gpt)\n",
    "    for pos_i in range(len(base.input_ids[0])):\n",
    "        _, counterfactual_outputs = intervenable(\n",
    "            base,\n",
    "            unit_locations={\n",
    "                \"sources->base\": pos_i\n",
    "            },\n",
    "            source_representations=rome_embedding,\n",
    "        )\n",
    "        distrib = embed_to_distrib(\n",
    "            gpt, counterfactual_outputs.last_hidden_state, logits=False\n",
    "        )\n",
    "        for token in tokens:\n",
    "            data.append(\n",
    "                {\n",
    "                    \"token\": format_token(tokenizer, token),\n",
    "                    \"prob\": float(distrib[0][-1][token]),\n",
    "                    \"layer\": f\"attn_o{till_layer_i}\",\n",
    "                    \"pos\": pos_i,\n",
    "                    \"type\": \"attention_output\",\n",
    "                }\n",
    "            )\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "81604a1c",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "df[\"layer\"] = df[\"layer\"].astype(\"category\")\n",
    "df[\"token\"] = df[\"token\"].astype(\"category\")\n",
    "nodes = []\n",
    "for l in range(gpt.config.n_layer - 1, -1, -1):\n",
    "    nodes.append(f\"mlp_o{l}\")\n",
    "    nodes.append(f\"attn_o{l}\")\n",
    "df[\"layer\"] = pd.Categorical(df[\"layer\"], categories=nodes[::-1], ordered=True)\n",
    "\n",
    "g = (\n",
    "    ggplot(df)\n",
    "    + geom_tile(aes(x=\"pos\", y=\"layer\", fill=\"prob\", color=\"prob\"))\n",
    "    + facet_wrap(\"~token\")\n",
    "    + theme(axis_text_x=element_text(rotation=90))\n",
    ")\n",
    "print(g)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
